Overview

Dataset statistics

Number of variables20
Number of observations57
Missing cells23
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory162.2 B

Variable types

Categorical6
Numeric14

Alerts

Country has a high cardinality: 57 distinct values High cardinality
Skrót has a high cardinality: 57 distinct values High cardinality
New_business_density is highly correlated with CorruptionHigh correlation
PDI is highly correlated with IDV and 4 other fieldsHigh correlation
IDV is highly correlated with PDI and 4 other fieldsHigh correlation
LTO is highly correlated with IVRHigh correlation
IVR is highly correlated with LTO and 2 other fieldsHigh correlation
Urban_population is highly correlated with PDI and 3 other fieldsHigh correlation
Alcohol_consumption is highly correlated with IDV and 2 other fieldsHigh correlation
Work_time is highly correlated with PDI and 4 other fieldsHigh correlation
Corruption is highly correlated with New_business_density and 5 other fieldsHigh correlation
Democracy is highly correlated with PDI and 6 other fieldsHigh correlation
New_business_density is highly correlated with CorruptionHigh correlation
PDI is highly correlated with IDV and 4 other fieldsHigh correlation
IDV is highly correlated with PDI and 4 other fieldsHigh correlation
IVR is highly correlated with Urban_population and 1 other fieldsHigh correlation
Urban_population is highly correlated with PDI and 3 other fieldsHigh correlation
Alcohol_consumption is highly correlated with IDV and 1 other fieldsHigh correlation
Work_time is highly correlated with PDI and 5 other fieldsHigh correlation
Corruption is highly correlated with New_business_density and 5 other fieldsHigh correlation
Democracy is highly correlated with PDI and 4 other fieldsHigh correlation
PDI is highly correlated with IDV and 2 other fieldsHigh correlation
IDV is highly correlated with PDI and 1 other fieldsHigh correlation
Work_time is highly correlated with DemocracyHigh correlation
Corruption is highly correlated with PDI and 1 other fieldsHigh correlation
Democracy is highly correlated with PDI and 3 other fieldsHigh correlation
Alcohol is highly correlated with Skrót and 1 other fieldsHigh correlation
Religion_2 is highly correlated with Skrót and 1 other fieldsHigh correlation
Skrót is highly correlated with Alcohol and 4 other fieldsHigh correlation
Religion is highly correlated with Skrót and 1 other fieldsHigh correlation
Cultural_cohesion is highly correlated with Skrót and 1 other fieldsHigh correlation
Country is highly correlated with Alcohol and 4 other fieldsHigh correlation
Country is highly correlated with Skrót and 18 other fieldsHigh correlation
Skrót is highly correlated with Country and 18 other fieldsHigh correlation
New_business_density is highly correlated with Country and 4 other fieldsHigh correlation
PDI is highly correlated with Country and 11 other fieldsHigh correlation
IDV is highly correlated with Country and 9 other fieldsHigh correlation
MAS is highly correlated with Country and 5 other fieldsHigh correlation
UAI is highly correlated with Country and 9 other fieldsHigh correlation
LTO is highly correlated with Country and 1 other fieldsHigh correlation
IVR is highly correlated with Country and 4 other fieldsHigh correlation
Urban_population is highly correlated with Country and 2 other fieldsHigh correlation
Alcohol_consumption is highly correlated with Country and 5 other fieldsHigh correlation
Alcohol is highly correlated with Country and 3 other fieldsHigh correlation
Cultural_cohesion is highly correlated with Country and 1 other fieldsHigh correlation
Religion is highly correlated with Country and 8 other fieldsHigh correlation
Religion_2 is highly correlated with Country and 4 other fieldsHigh correlation
Work_time is highly correlated with Country and 7 other fieldsHigh correlation
Corruption is highly correlated with Country and 7 other fieldsHigh correlation
Education is highly correlated with Country and 4 other fieldsHigh correlation
Democracy is highly correlated with Country and 8 other fieldsHigh correlation
Vacc is highly correlated with Country and 2 other fieldsHigh correlation
LTO has 2 (3.5%) missing values Missing
IVR has 3 (5.3%) missing values Missing
Religion_2 has 11 (19.3%) missing values Missing
Work_time has 5 (8.8%) missing values Missing
Education has 2 (3.5%) missing values Missing
Country is uniformly distributed Uniform
Skrót is uniformly distributed Uniform
Country has unique values Unique
Skrót has unique values Unique
New_business_density has unique values Unique
Urban_population has unique values Unique
Education has 1 (1.8%) zeros Zeros
Democracy has 14 (24.6%) zeros Zeros

Reproduction

Analysis started2022-05-27 20:43:04.529696
Analysis finished2022-05-27 20:43:18.223872
Duration13.69 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size584.0 B
Albania
 
1
Latvia
 
1
Luxembourg
 
1
Malaysia
 
1
Malta
 
1
Other values (52)
52 

Length

Max length22
Median length14
Mean length8
Min length4

Characters and Unicode

Total characters456
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st rowAlbania
2nd rowAlgeria
3rd rowArgentina
4th rowArmenia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Albania1
 
1.8%
Latvia1
 
1.8%
Luxembourg1
 
1.8%
Malaysia1
 
1.8%
Malta1
 
1.8%
Mexico1
 
1.8%
Moldova1
 
1.8%
Morocco1
 
1.8%
Netherlands1
 
1.8%
New Zealand1
 
1.8%
Other values (47)47
82.5%

Length

2022-05-27T22:43:18.264303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
albania1
 
1.6%
latvia1
 
1.6%
argentina1
 
1.6%
armenia1
 
1.6%
australia1
 
1.6%
azerbaijan1
 
1.6%
bangladesh1
 
1.6%
belgium1
 
1.6%
bosnia1
 
1.6%
and1
 
1.6%
Other values (54)54
84.4%

Most occurring characters

ValueCountFrequency (%)
a68
14.9%
e38
 
8.3%
i37
 
8.1%
n36
 
7.9%
r30
 
6.6%
l27
 
5.9%
o21
 
4.6%
d16
 
3.5%
u15
 
3.3%
t14
 
3.1%
Other values (35)154
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter386
84.6%
Uppercase Letter63
 
13.8%
Space Separator7
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a68
17.6%
e38
9.8%
i37
9.6%
n36
9.3%
r30
 
7.8%
l27
 
7.0%
o21
 
5.4%
d16
 
4.1%
u15
 
3.9%
t14
 
3.6%
Other values (14)84
21.8%
Uppercase Letter
ValueCountFrequency (%)
S6
 
9.5%
A6
 
9.5%
M5
 
7.9%
B5
 
7.9%
P5
 
7.9%
C4
 
6.3%
I4
 
6.3%
N3
 
4.8%
U3
 
4.8%
G3
 
4.8%
Other values (10)19
30.2%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin449
98.5%
Common7
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a68
15.1%
e38
 
8.5%
i37
 
8.2%
n36
 
8.0%
r30
 
6.7%
l27
 
6.0%
o21
 
4.7%
d16
 
3.6%
u15
 
3.3%
t14
 
3.1%
Other values (34)147
32.7%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a68
14.9%
e38
 
8.3%
i37
 
8.1%
n36
 
7.9%
r30
 
6.6%
l27
 
5.9%
o21
 
4.6%
d16
 
3.5%
u15
 
3.3%
t14
 
3.1%
Other values (35)154
33.8%

Skrót
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size584.0 B
ALB
 
1
LVA
 
1
LUX
 
1
MYS
 
1
MLT
 
1
Other values (52)
52 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters171
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st rowALB
2nd rowDZA
3rd rowARG
4th rowARM
5th rowAUS

Common Values

ValueCountFrequency (%)
ALB1
 
1.8%
LVA1
 
1.8%
LUX1
 
1.8%
MYS1
 
1.8%
MLT1
 
1.8%
MEX1
 
1.8%
MDA1
 
1.8%
MAR1
 
1.8%
NLD1
 
1.8%
NZL1
 
1.8%
Other values (47)47
82.5%

Length

2022-05-27T22:43:18.326430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alb1
 
1.8%
dza1
 
1.8%
arg1
 
1.8%
arm1
 
1.8%
aus1
 
1.8%
aze1
 
1.8%
bgd1
 
1.8%
bel1
 
1.8%
bih1
 
1.8%
bra1
 
1.8%
Other values (47)47
82.5%

Most occurring characters

ValueCountFrequency (%)
R20
 
11.7%
L14
 
8.2%
A14
 
8.2%
S10
 
5.8%
E10
 
5.8%
U10
 
5.8%
N9
 
5.3%
T8
 
4.7%
B8
 
4.7%
P8
 
4.7%
Other values (15)60
35.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter171
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R20
 
11.7%
L14
 
8.2%
A14
 
8.2%
S10
 
5.8%
E10
 
5.8%
U10
 
5.8%
N9
 
5.3%
T8
 
4.7%
B8
 
4.7%
P8
 
4.7%
Other values (15)60
35.1%

Most occurring scripts

ValueCountFrequency (%)
Latin171
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R20
 
11.7%
L14
 
8.2%
A14
 
8.2%
S10
 
5.8%
E10
 
5.8%
U10
 
5.8%
N9
 
5.3%
T8
 
4.7%
B8
 
4.7%
P8
 
4.7%
Other values (15)60
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R20
 
11.7%
L14
 
8.2%
A14
 
8.2%
S10
 
5.8%
E10
 
5.8%
U10
 
5.8%
N9
 
5.3%
T8
 
4.7%
B8
 
4.7%
P8
 
4.7%
Other values (15)60
35.1%

New_business_density
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.005301409
Minimum0.04129152124
Maximum23.59313375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:18.389815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.04129152124
5-th percentile0.2717950119
Q11.420295093
median3.259612362
Q37.129563049
95-th percentile17.25402195
Maximum23.59313375
Range23.55184223
Interquartile range (IQR)5.709267956

Descriptive statistics

Standard deviation5.297913427
Coefficient of variation (CV)1.058460419
Kurtosis2.466382752
Mean5.005301409
Median Absolute Deviation (MAD)2.132107388
Skewness1.6666942
Sum285.3021803
Variance28.06788668
MonotonicityNot monotonic
2022-05-27T22:43:18.465310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.520986821
 
1.8%
8.0050120591
 
1.8%
17.196311821
 
1.8%
2.3660841091
 
1.8%
17.484862481
 
1.8%
1.0040703851
 
1.8%
1.8631592751
 
1.8%
1.9042857191
 
1.8%
6.4166495781
 
1.8%
17.838204511
 
1.8%
Other values (47)47
82.5%
ValueCountFrequency (%)
0.041291521241
1.8%
0.13724083941
1.8%
0.19862993361
1.8%
0.29008628151
1.8%
0.35329661891
1.8%
0.38695672021
1.8%
0.52774834971
1.8%
0.56600997931
1.8%
1.0040703851
1.8%
1.0907186541
1.8%
ValueCountFrequency (%)
23.593133751
1.8%
17.838204511
1.8%
17.484862481
1.8%
17.196311821
1.8%
15.645052991
1.8%
14.472167881
1.8%
10.313920331
1.8%
10.101552861
1.8%
10.010788471
1.8%
10.009630161
1.8%

PDI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.84210526
Minimum13
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:18.538575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile26.8
Q142
median64
Q380
95-th percentile94.2
Maximum100
Range87
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.66431231
Coefficient of variation (CV)0.3664867523
Kurtosis-0.9003937637
Mean61.84210526
Median Absolute Deviation (MAD)20
Skewness-0.2229601058
Sum3525
Variance513.6710526
MonotonicityNot monotonic
2022-05-27T22:43:18.605265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
904
 
7.0%
572
 
3.5%
802
 
3.5%
952
 
3.5%
312
 
3.5%
352
 
3.5%
682
 
3.5%
642
 
3.5%
402
 
3.5%
662
 
3.5%
Other values (31)35
61.4%
ValueCountFrequency (%)
131
1.8%
181
1.8%
221
1.8%
281
1.8%
312
3.5%
331
1.8%
341
1.8%
352
3.5%
382
3.5%
402
3.5%
ValueCountFrequency (%)
1001
 
1.8%
952
3.5%
941
 
1.8%
931
 
1.8%
921
 
1.8%
904
7.0%
861
 
1.8%
852
3.5%
811
 
1.8%
802
3.5%

IDV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.24561404
Minimum6
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:18.674885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15.4
Q125
median39
Q367
95-th percentile80
Maximum90
Range84
Interquartile range (IQR)42

Descriptive statistics

Standard deviation22.83111966
Coefficient of variation (CV)0.5046040406
Kurtosis-1.208308116
Mean45.24561404
Median Absolute Deviation (MAD)19
Skewness0.2393900966
Sum2579
Variance521.2600251
MonotonicityNot monotonic
2022-05-27T22:43:18.738048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
204
 
7.0%
604
 
7.0%
463
 
5.3%
223
 
5.3%
303
 
5.3%
252
 
3.5%
272
 
3.5%
702
 
3.5%
352
 
3.5%
712
 
3.5%
Other values (29)30
52.6%
ValueCountFrequency (%)
61
 
1.8%
111
 
1.8%
131
 
1.8%
161
 
1.8%
191
 
1.8%
204
7.0%
223
5.3%
231
 
1.8%
252
3.5%
261
 
1.8%
ValueCountFrequency (%)
901
1.8%
891
1.8%
802
3.5%
791
1.8%
761
1.8%
751
1.8%
741
1.8%
712
3.5%
702
3.5%
691
1.8%

MAS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.71929825
Minimum5
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:18.804910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q137
median47
Q357
95-th percentile72
Maximum95
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.78806079
Coefficient of variation (CV)0.4021477524
Kurtosis0.3198733475
Mean46.71929825
Median Absolute Deviation (MAD)10
Skewness-0.01292664537
Sum2663
Variance352.9912281
MonotonicityNot monotonic
2022-05-27T22:43:18.869882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
504
 
7.0%
403
 
5.3%
643
 
5.3%
423
 
5.3%
572
 
3.5%
432
 
3.5%
562
 
3.5%
702
 
3.5%
472
 
3.5%
482
 
3.5%
Other values (31)32
56.1%
ValueCountFrequency (%)
51
1.8%
81
1.8%
91
1.8%
141
1.8%
161
1.8%
191
1.8%
261
1.8%
271
1.8%
281
1.8%
301
1.8%
ValueCountFrequency (%)
951
 
1.8%
881
 
1.8%
801
 
1.8%
702
3.5%
691
 
1.8%
681
 
1.8%
662
3.5%
643
5.3%
611
 
1.8%
581
 
1.8%

UAI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.56140351
Minimum8
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:18.936972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile33.8
Q160
median80
Q388
95-th percentile98
Maximum100
Range92
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.03407875
Coefficient of variation (CV)0.3036611434
Kurtosis0.2066676275
Mean72.56140351
Median Absolute Deviation (MAD)14
Skewness-0.9354320599
Sum4136
Variance485.5006266
MonotonicityNot monotonic
2022-05-27T22:43:19.005245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
865
 
8.8%
703
 
5.3%
953
 
5.3%
352
 
3.5%
982
 
3.5%
652
 
3.5%
922
 
3.5%
802
 
3.5%
852
 
3.5%
872
 
3.5%
Other values (28)32
56.1%
ValueCountFrequency (%)
81
1.8%
231
1.8%
291
1.8%
352
3.5%
361
1.8%
401
1.8%
441
1.8%
491
1.8%
501
1.8%
511
1.8%
ValueCountFrequency (%)
1001
 
1.8%
991
 
1.8%
982
3.5%
961
 
1.8%
953
5.3%
942
3.5%
931
 
1.8%
922
3.5%
901
 
1.8%
882
3.5%

LTO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)69.1%
Missing2
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean50.14545455
Minimum13
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:19.073718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q132.5
median51
Q368
95-th percentile82.3
Maximum88
Range75
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation20.92881956
Coefficient of variation (CV)0.4173622465
Kurtosis-1.079980211
Mean50.14545455
Median Absolute Deviation (MAD)18
Skewness0.05715548033
Sum2758
Variance438.0154882
MonotonicityNot monotonic
2022-05-27T22:43:19.143940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
614
 
7.0%
823
 
5.3%
383
 
5.3%
582
 
3.5%
522
 
3.5%
262
 
3.5%
512
 
3.5%
352
 
3.5%
242
 
3.5%
692
 
3.5%
Other values (28)31
54.4%
ValueCountFrequency (%)
131
1.8%
141
1.8%
202
3.5%
211
1.8%
242
3.5%
251
1.8%
262
3.5%
271
1.8%
281
1.8%
311
1.8%
ValueCountFrequency (%)
881
 
1.8%
861
 
1.8%
831
 
1.8%
823
5.3%
811
 
1.8%
741
 
1.8%
721
 
1.8%
711
 
1.8%
702
3.5%
692
3.5%

IVR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)74.1%
Missing3
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean44.98148148
Minimum13
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:19.220547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15.65
Q126.5
median44.5
Q361.25
95-th percentile79.75
Maximum97
Range84
Interquartile range (IQR)34.75

Descriptive statistics

Standard deviation21.68252121
Coefficient of variation (CV)0.4820321718
Kurtosis-0.7595639394
Mean44.98148148
Median Absolute Deviation (MAD)18
Skewness0.3296611421
Sum2429
Variance470.1317261
MonotonicityNot monotonic
2022-05-27T22:43:19.286969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
163
 
5.3%
203
 
5.3%
573
 
5.3%
292
 
3.5%
332
 
3.5%
422
 
3.5%
682
 
3.5%
442
 
3.5%
662
 
3.5%
252
 
3.5%
Other values (30)31
54.4%
(Missing)3
 
5.3%
ValueCountFrequency (%)
131
 
1.8%
141
 
1.8%
151
 
1.8%
163
5.3%
191
 
1.8%
203
5.3%
221
 
1.8%
252
3.5%
261
 
1.8%
281
 
1.8%
ValueCountFrequency (%)
971
1.8%
891
1.8%
831
1.8%
781
1.8%
751
1.8%
711
1.8%
701
1.8%
691
1.8%
682
3.5%
662
3.5%

Urban_population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.95173684
Minimum34.03
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:19.358687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum34.03
5-th percentile46.0514
Q163.149
median74.433
Q386.012
95-th percentile94.7564
Maximum100
Range65.97
Interquartile range (IQR)22.863

Descriptive statistics

Standard deviation15.81173045
Coefficient of variation (CV)0.2167423441
Kurtosis-0.3090373055
Mean72.95173684
Median Absolute Deviation (MAD)11.579
Skewness-0.4995096144
Sum4158.249
Variance250.0108197
MonotonicityNot monotonic
2022-05-27T22:43:19.433328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.3191
 
1.8%
68.1421
 
1.8%
90.9811
 
1.8%
76.0361
 
1.8%
94.6121
 
1.8%
80.1561
 
1.8%
42.6291
 
1.8%
62.4531
 
1.8%
91.491
 
1.8%
86.5381
 
1.8%
Other values (47)47
82.5%
ValueCountFrequency (%)
34.031
1.8%
36.6321
1.8%
42.6291
1.8%
46.9071
1.8%
48.2451
1.8%
49.9491
1.8%
51.0541
1.8%
53.9981
1.8%
55.681
1.8%
56.0921
1.8%
ValueCountFrequency (%)
1001
1.8%
98.0011
1.8%
95.3341
1.8%
94.6121
1.8%
92.4181
1.8%
91.871
1.8%
91.6161
1.8%
91.491
1.8%
90.9811
1.8%
87.8741
1.8%

Alcohol_consumption
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.391035088
Minimum0.019
Maximum14.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:19.512769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.019
5-th percentile0.93
Q16.38
median8.93
Q311.43
95-th percentile12.916
Maximum14.45
Range14.431
Interquartile range (IQR)5.05

Descriptive statistics

Standard deviation3.703530362
Coefficient of variation (CV)0.4413675218
Kurtosis-0.4245339106
Mean8.391035088
Median Absolute Deviation (MAD)2.52
Skewness-0.624320832
Sum478.289
Variance13.71613714
MonotonicityNot monotonic
2022-05-27T22:43:19.585957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.232
 
3.5%
7.171
 
1.8%
11.711
 
1.8%
0.851
 
1.8%
7.991
 
1.8%
51
 
1.8%
11.431
 
1.8%
0.691
 
1.8%
9.611
 
1.8%
10.631
 
1.8%
Other values (46)46
80.7%
ValueCountFrequency (%)
0.0191
1.8%
0.691
1.8%
0.851
1.8%
0.951
1.8%
2.031
1.8%
2.051
1.8%
2.451
1.8%
3.891
1.8%
4.211
1.8%
4.411
1.8%
ValueCountFrequency (%)
14.451
1.8%
13.221
1.8%
12.941
1.8%
12.911
1.8%
12.881
1.8%
12.771
1.8%
12.721
1.8%
12.651
1.8%
12.331
1.8%
12.031
1.8%

Alcohol
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size584.0 B
Beer
34 
Wine
12 
Vodka
11 

Length

Max length5
Median length4
Mean length4.192982456
Min length4

Characters and Unicode

Total characters239
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVodka
2nd rowBeer
3rd rowWine
4th rowVodka
5th rowBeer

Common Values

ValueCountFrequency (%)
Beer34
59.6%
Wine12
 
21.1%
Vodka11
 
19.3%

Length

2022-05-27T22:43:19.652808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T22:43:19.711483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
beer34
59.6%
wine12
 
21.1%
vodka11
 
19.3%

Most occurring characters

ValueCountFrequency (%)
e80
33.5%
B34
14.2%
r34
14.2%
W12
 
5.0%
i12
 
5.0%
n12
 
5.0%
V11
 
4.6%
o11
 
4.6%
d11
 
4.6%
k11
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter182
76.2%
Uppercase Letter57
 
23.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e80
44.0%
r34
18.7%
i12
 
6.6%
n12
 
6.6%
o11
 
6.0%
d11
 
6.0%
k11
 
6.0%
a11
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
B34
59.6%
W12
 
21.1%
V11
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Latin239
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e80
33.5%
B34
14.2%
r34
14.2%
W12
 
5.0%
i12
 
5.0%
n12
 
5.0%
V11
 
4.6%
o11
 
4.6%
d11
 
4.6%
k11
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e80
33.5%
B34
14.2%
r34
14.2%
W12
 
5.0%
i12
 
5.0%
n12
 
5.0%
V11
 
4.6%
o11
 
4.6%
d11
 
4.6%
k11
 
4.6%

Cultural_cohesion
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size584.0 B
Multicultural
47 
Monoculture
10 

Length

Max length13
Median length13
Mean length12.64912281
Min length11

Characters and Unicode

Total characters721
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonoculture
2nd rowMonoculture
3rd rowMonoculture
4th rowMulticultural
5th rowMulticultural

Common Values

ValueCountFrequency (%)
Multicultural47
82.5%
Monoculture10
 
17.5%

Length

2022-05-27T22:43:19.766919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T22:43:19.828167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
multicultural47
82.5%
monoculture10
 
17.5%

Most occurring characters

ValueCountFrequency (%)
u161
22.3%
l151
20.9%
t104
14.4%
M57
 
7.9%
c57
 
7.9%
r57
 
7.9%
i47
 
6.5%
a47
 
6.5%
o20
 
2.8%
n10
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter664
92.1%
Uppercase Letter57
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u161
24.2%
l151
22.7%
t104
15.7%
c57
 
8.6%
r57
 
8.6%
i47
 
7.1%
a47
 
7.1%
o20
 
3.0%
n10
 
1.5%
e10
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
M57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin721
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u161
22.3%
l151
20.9%
t104
14.4%
M57
 
7.9%
c57
 
7.9%
r57
 
7.9%
i47
 
6.5%
a47
 
6.5%
o20
 
2.8%
n10
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u161
22.3%
l151
20.9%
t104
14.4%
M57
 
7.9%
c57
 
7.9%
r57
 
7.9%
i47
 
6.5%
a47
 
6.5%
o20
 
2.8%
n10
 
1.4%

Religion
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size584.0 B
Roman Catholic
24 
Orthodox Christianity
Islam
Evangelical Church
Irreligion

Length

Max length21
Median length18
Mean length13.12280702
Min length5

Characters and Unicode

Total characters748
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIslam
2nd rowIslam
3rd rowRoman Catholic
4th rowOrthodox Christianity
5th rowEvangelical Church

Common Values

ValueCountFrequency (%)
Roman Catholic24
42.1%
Orthodox Christianity9
 
15.8%
Islam8
 
14.0%
Evangelical Church6
 
10.5%
Irreligion5
 
8.8%
Other5
 
8.8%

Length

2022-05-27T22:43:19.881096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T22:43:19.948671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
roman24
25.0%
catholic24
25.0%
orthodox9
 
9.4%
christianity9
 
9.4%
islam8
 
8.3%
evangelical6
 
6.2%
church6
 
6.2%
irreligion5
 
5.2%
other5
 
5.2%

Most occurring characters

ValueCountFrequency (%)
a77
 
10.3%
o71
 
9.5%
i67
 
9.0%
h59
 
7.9%
t56
 
7.5%
l49
 
6.6%
n44
 
5.9%
39
 
5.2%
C39
 
5.2%
r39
 
5.2%
Other values (14)208
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter613
82.0%
Uppercase Letter96
 
12.8%
Space Separator39
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a77
12.6%
o71
11.6%
i67
10.9%
h59
9.6%
t56
9.1%
l49
8.0%
n44
7.2%
r39
6.4%
c36
5.9%
m32
 
5.2%
Other values (8)83
13.5%
Uppercase Letter
ValueCountFrequency (%)
C39
40.6%
R24
25.0%
O14
 
14.6%
I13
 
13.5%
E6
 
6.2%
Space Separator
ValueCountFrequency (%)
39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin709
94.8%
Common39
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a77
10.9%
o71
10.0%
i67
 
9.4%
h59
 
8.3%
t56
 
7.9%
l49
 
6.9%
n44
 
6.2%
C39
 
5.5%
r39
 
5.5%
c36
 
5.1%
Other values (13)172
24.3%
Common
ValueCountFrequency (%)
39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a77
 
10.3%
o71
 
9.5%
i67
 
9.0%
h59
 
7.9%
t56
 
7.5%
l49
 
6.6%
n44
 
5.9%
39
 
5.2%
C39
 
5.2%
r39
 
5.2%
Other values (14)208
27.8%

Religion_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)13.0%
Missing11
Missing (%)19.3%
Memory size584.0 B
Irreligion
23 
Evangelical Church
Roman Catholic
Other
Islam

Length

Max length21
Median length19.5
Mean length11.34782609
Min length5

Characters and Unicode

Total characters522
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoman Catholic
2nd rowIrreligion
3rd rowIrreligion
4th rowOther
5th rowIrreligion

Common Values

ValueCountFrequency (%)
Irreligion23
40.4%
Evangelical Church7
 
12.3%
Roman Catholic6
 
10.5%
Other5
 
8.8%
Islam3
 
5.3%
Orthodox Christianity2
 
3.5%
(Missing)11
19.3%

Length

2022-05-27T22:43:20.015319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T22:43:20.080338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
irreligion23
37.7%
evangelical7
 
11.5%
church7
 
11.5%
roman6
 
9.8%
catholic6
 
9.8%
other5
 
8.2%
islam3
 
4.9%
orthodox2
 
3.3%
christianity2
 
3.3%

Most occurring characters

ValueCountFrequency (%)
i65
12.5%
r62
11.9%
l46
 
8.8%
o39
 
7.5%
n38
 
7.3%
e35
 
6.7%
a31
 
5.9%
g30
 
5.7%
h29
 
5.6%
I26
 
5.0%
Other values (14)121
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter446
85.4%
Uppercase Letter61
 
11.7%
Space Separator15
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i65
14.6%
r62
13.9%
l46
10.3%
o39
8.7%
n38
8.5%
e35
7.8%
a31
7.0%
g30
6.7%
h29
6.5%
c20
 
4.5%
Other values (8)51
11.4%
Uppercase Letter
ValueCountFrequency (%)
I26
42.6%
C15
24.6%
E7
 
11.5%
O7
 
11.5%
R6
 
9.8%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin507
97.1%
Common15
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i65
12.8%
r62
12.2%
l46
9.1%
o39
 
7.7%
n38
 
7.5%
e35
 
6.9%
a31
 
6.1%
g30
 
5.9%
h29
 
5.7%
I26
 
5.1%
Other values (13)106
20.9%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i65
12.5%
r62
11.9%
l46
 
8.8%
o39
 
7.5%
n38
 
7.3%
e35
 
6.7%
a31
 
5.9%
g30
 
5.7%
h29
 
5.6%
I26
 
5.0%
Other values (14)121
23.2%

Work_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct52
Distinct (%)100.0%
Missing5
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean1850.069231
Minimum1353.89
Maximum2255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:20.152485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1353.89
5-th percentile1424.3725
Q11682.4425
median1850.35
Q32031.3825
95-th percentile2237.8515
Maximum2255
Range901.11
Interquartile range (IQR)348.94

Descriptive statistics

Standard deviation247.2139262
Coefficient of variation (CV)0.1336241488
Kurtosis-0.8572027812
Mean1850.069231
Median Absolute Deviation (MAD)179.115
Skewness-0.1032938746
Sum96203.6
Variance61114.72532
MonotonicityNot monotonic
2022-05-27T22:43:20.225865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2113.651
 
1.8%
1932.461
 
1.8%
1874.61
 
1.8%
1844.021
 
1.8%
1518.861
 
1.8%
2238.271
 
1.8%
2040.031
 
1.8%
22551
 
1.8%
1952.391
 
1.8%
1430.021
 
1.8%
Other values (42)42
73.7%
(Missing)5
 
8.8%
ValueCountFrequency (%)
1353.891
1.8%
1400.381
1.8%
1417.471
1.8%
1430.021
1.8%
1514.141
1.8%
1518.861
1.8%
1544.271
1.8%
1552.351
1.8%
1589.681
1.8%
1609.291
1.8%
ValueCountFrequency (%)
22551
1.8%
2238.271
1.8%
22381
1.8%
2237.731
1.8%
2232.351
1.8%
2185.451
1.8%
2165.41
1.8%
2148.561
1.8%
2141.941
1.8%
2117.011
1.8%

Corruption
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.63157895
Minimum26
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:20.297488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile31.2
Q136
median49
Q372
95-th percentile87.2
Maximum90
Range64
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.19337902
Coefficient of variation (CV)0.3696283251
Kurtosis-1.274577515
Mean54.63157895
Median Absolute Deviation (MAD)15
Skewness0.4045000333
Sum3114
Variance407.7725564
MonotonicityNot monotonic
2022-05-27T22:43:20.362960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
344
 
7.0%
364
 
7.0%
493
 
5.3%
383
 
5.3%
432
 
3.5%
872
 
3.5%
902
 
3.5%
722
 
3.5%
332
 
3.5%
422
 
3.5%
Other values (30)31
54.4%
ValueCountFrequency (%)
261
 
1.8%
271
 
1.8%
281
 
1.8%
321
 
1.8%
332
3.5%
344
7.0%
351
 
1.8%
364
7.0%
371
 
1.8%
383
5.3%
ValueCountFrequency (%)
902
3.5%
881
1.8%
872
3.5%
861
1.8%
852
3.5%
841
1.8%
801
1.8%
791
1.8%
751
1.8%
741
1.8%

Education
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct55
Distinct (%)100.0%
Missing2
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean12.16254545
Minimum0
Maximum29.71
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:20.437022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.693
Q16.265
median12.27
Q315.615
95-th percentile24.607
Maximum29.71
Range29.71
Interquartile range (IQR)9.35

Descriptive statistics

Standard deviation6.827688514
Coefficient of variation (CV)0.5613700306
Kurtosis-0.2611133336
Mean12.16254545
Median Absolute Deviation (MAD)5.61
Skewness0.386337614
Sum668.94
Variance46.61733044
MonotonicityNot monotonic
2022-05-27T22:43:20.509700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.931
 
1.8%
11.371
 
1.8%
5.811
 
1.8%
10.521
 
1.8%
9.811
 
1.8%
8.761
 
1.8%
5.351
 
1.8%
15.631
 
1.8%
15.51
 
1.8%
12.161
 
1.8%
Other values (45)45
78.9%
(Missing)2
 
3.5%
ValueCountFrequency (%)
01
1.8%
0.931
1.8%
2.281
1.8%
2.871
1.8%
3.261
1.8%
3.51
1.8%
3.921
1.8%
4.851
1.8%
5.281
1.8%
5.351
1.8%
ValueCountFrequency (%)
29.711
1.8%
26.81
1.8%
24.741
1.8%
24.551
1.8%
22.51
1.8%
20.261
1.8%
18.911
1.8%
18.871
1.8%
18.551
1.8%
18.541
1.8%

Democracy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.87719298
Minimum0
Maximum170
Zeros14
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:20.579808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median29
Q370
95-th percentile107.2
Maximum170
Range170
Interquartile range (IQR)61

Descriptive statistics

Standard deviation40.49341665
Coefficient of variation (CV)0.9906114803
Kurtosis1.647036159
Mean40.87719298
Median Absolute Deviation (MAD)29
Skewness1.291554555
Sum2330
Variance1639.716792
MonotonicityNot monotonic
2022-05-27T22:43:20.642306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
014
24.6%
295
 
8.8%
284
 
7.0%
734
 
7.0%
723
 
5.3%
702
 
3.5%
972
 
3.5%
222
 
3.5%
411
 
1.8%
671
 
1.8%
Other values (19)19
33.3%
ValueCountFrequency (%)
014
24.6%
91
 
1.8%
141
 
1.8%
181
 
1.8%
191
 
1.8%
201
 
1.8%
222
 
3.5%
231
 
1.8%
261
 
1.8%
284
 
7.0%
ValueCountFrequency (%)
1701
 
1.8%
1611
 
1.8%
1321
 
1.8%
1011
 
1.8%
1001
 
1.8%
972
3.5%
734
7.0%
723
5.3%
702
3.5%
671
 
1.8%

Vacc
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.77175439
Minimum27.61
Maximum91.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2022-05-27T22:43:20.710936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27.61
5-th percentile34.55
Q166.3
median72.22
Q378.81
95-th percentile83.582
Maximum91.76
Range64.15
Interquartile range (IQR)12.51

Descriptive statistics

Standard deviation15.50409567
Coefficient of variation (CV)0.2254427826
Kurtosis1.219954864
Mean68.77175439
Median Absolute Deviation (MAD)6.59
Skewness-1.371449566
Sum3919.99
Variance240.3769826
MonotonicityNot monotonic
2022-05-27T22:43:20.778170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
69.68
 
14.0%
75.034
 
7.0%
78.813
 
5.3%
83.283
 
5.3%
38.052
 
3.5%
51.192
 
3.5%
66.32
 
3.5%
27.612
 
3.5%
78.32
 
3.5%
72.221
 
1.8%
Other values (28)28
49.1%
ValueCountFrequency (%)
27.612
3.5%
28.911
1.8%
35.961
1.8%
38.052
3.5%
43.531
1.8%
50.531
1.8%
51.192
3.5%
62.151
1.8%
64.221
1.8%
64.491
1.8%
ValueCountFrequency (%)
91.761
 
1.8%
87.071
 
1.8%
84.191
 
1.8%
83.431
 
1.8%
83.283
5.3%
83.231
 
1.8%
81.61
 
1.8%
80.861
 
1.8%
80.41
 
1.8%
79.761
 
1.8%

Interactions

2022-05-27T22:43:16.801938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.271176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.015952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.746369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.469913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.181461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.912464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.719674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.457983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.235069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.760889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.501810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.272709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.028786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.854001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.328590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.066036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.796204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.518650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.232755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.968316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.769773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.510940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.290220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.814571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.554058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.323866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.083308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.907423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.378147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.114779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.844163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.567249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.282615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.022143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.818702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.563948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.345306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.865626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.607657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.374842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.135810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.957742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.425948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.162569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.889237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.613963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.329950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.083324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.875756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.614675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.398506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.913993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.657175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.424218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.186798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.007579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.473843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.212685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.934862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.659521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.375885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.135483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.925623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.665073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.451550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.960571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.709852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.474259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.237685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.059547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.523163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.262242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.983802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.707219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.424262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.189348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.974803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.719248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.505858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.010135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.762571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.524865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.290472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.118513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.581283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.318662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.048734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.763148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.480195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.254158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.031219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.780058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.298826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.067640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.823289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.584169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.350714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.170057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.632481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.367393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.103301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.811057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.530035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.309117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.079904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.834037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.354189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.118216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.875629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.638301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.403363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.226226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.687676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.422139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.155959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.864175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.585334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.369201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.133052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.891029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.413014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.173407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.933603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.694795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.460650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.283491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.744129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.479195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.209857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.920655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.643439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.431440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.190123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.951364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.472778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.230879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.992597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.752526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.520402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.334824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.796028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.529383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.260233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.968633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.696031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.485795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.241256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.002796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.527427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.283321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.045488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.804325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.572590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.389236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.851742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.583596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.314376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.021571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.750989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.544070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.295065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.060247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.586796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.339076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.101951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.861385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.630539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.444766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.905983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.637455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.365634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.074616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.804286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.601483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.348597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.116549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.644247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.392103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.158694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.916517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.687181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:17.500932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:06.961391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:07.694551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:08.419297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.128409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:09.859350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:10.661270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:11.404440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:12.176017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:13.702751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:14.447719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.216917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:15.973403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T22:43:16.745097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-27T22:43:20.846169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-27T22:43:20.950803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-27T22:43:21.050490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-27T22:43:21.144885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-27T22:43:21.225759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-27T22:43:17.802505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-27T22:43:17.995727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-27T22:43:18.091578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-27T22:43:18.158024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CountrySkrótNew_business_densityPDIIDVMASUAILTOIVRUrban_populationAlcohol_consumptionAlcoholCultural_cohesionReligionReligion_2Work_timeCorruptionEducationDemocracyVacc
0AlbaniaALB1.529020807061.0015.0060.327.17VodkaMonocultureIslamRoman Catholic2113.65330.931443.53
1AlgeriaDZA0.358035357026.0032.0072.630.95BeerMonocultureIslamNaN2165.40346.66083.28
2ArgentinaARG0.204946568620.0062.0091.879.65WineMonocultureRoman CatholicIrreligion1691.54352.873587.07
3ArmeniaARM3.058522508861.0025.0063.155.55VodkaMulticulturalOrthodox ChristianityNaN1977.253415.03035.96
4AustraliaAUS14.473890615121.0071.0086.0110.51BeerMulticulturalEvangelical ChurchIrreligion1731.498518.5216184.19
5AzerbaijanAZE1.668522508861.0022.0055.684.41BeerMulticulturalIslamNaN1916.6927NaN051.19
6BangladeshBGD0.048020556047.0020.0036.630.02BeerMonocultureIslamOther2232.35262.28062.15
7BelgiumBEL3.376575549482.0057.0098.0011.08BeerMulticulturalRoman CatholicIrreligion1544.277517.697251.19
8Bosnia and HerzegovinaBIH1.099022488770.0044.0048.247.15BeerMulticulturalIslamOrthodox Christianity2141.9442NaN2228.91
9BrazilBRA1.296938497644.0059.0086.577.42BeerMulticulturalRoman CatholicEvangelical Church1709.49435.633227.61

Last rows

CountrySkrótNew_business_densityPDIIDVMASUAILTOIVRUrban_populationAlcohol_consumptionAlcoholCultural_cohesionReligionReligion_2Work_timeCorruptionEducationDemocracyVacc
47SerbiaSRB1.888625439252.0028.0056.098.75WineMulticulturalOrthodox ChristianityNaNNaN398.97069.60
48SingaporeSGP10.01742048872.0046.00100.002.03BeerMulticulturalOtherIrreligion2237.738729.71069.60
49SpainESP3.075751428648.0044.0080.3212.72BeerMulticulturalRoman CatholicIrreligion1686.506514.964183.28
50SwedenSWE7.18317152953.0078.0087.438.93WineMulticulturalEvangelical ChurchIrreligion1609.298814.939769.60
51SwitzerlandCHE4.533468705874.0066.0073.8011.53WineMulticulturalRoman CatholicIrreligion1589.688617.8817027.61
52ThailandTHA1.136420346432.0045.0049.958.30VodkaMulticulturalOtherNaN2185.453710.47074.91
53TurkeyTUR1.566637458546.0049.0075.142.05BeerMulticulturalIslamIrreligion1832.00495.28067.54
54UkraineUKR1.689225279586.0014.0069.358.32VodkaMulticulturalOrthodox ChristianityRoman CatholicNaN3224.55076.79
55United KingdomGBR15.653589663551.0069.0083.4011.45BeerMulticulturalOtherIrreligion1670.277415.3110075.20
56UruguayURY1.266136389826.0053.0095.336.92WineMulticulturalRoman CatholicIrreligion1552.35723.503483.43